Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Ping-Shou Zhong

Zhong
Michigan State University
Room 306, Statistics

Structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the data (p) is smaller than the sample size (n). However, this assessment becomes quite challenging when p is bigger than n, since the classical likelihood-ratio test is no longer applicable. In this talk, an adjusted goodness-of-fit test will be introduced to examine a broad range of covariance structures under the scenario of “large p, small n”. Some analytical examples and large sample properties will be presented to illustrate the effectiveness of adjustment for assessing the goodness-of-fit of covariance. In addition, numerical examples and a real data application will be provided to demonstrate the performance and the practical utility of the proposed method.


More information about Ping-Shou Zhong may be found at http://www.stt.msu.edu/People/people.aspx?member=pszhong

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.